Cryptographic policy enforcement

ABSTRACT

Objects can be extracted from data flows captured by a capture device. In one embodiment, the invention includes assigning to each captured object a cryptographic status based on whether the captured object is encrypted. In one embodiment, the invention further includes determining whether the object violated a cryptographic policy using the assigned cryptographic status of the object.

PRIORITY AND RELATED APPLICATIONS

This application is a continuation (and claims the benefit of priorityunder 35 U.S.C. §120) of U.S. application Ser. No. 10/995,455, filedNov. 22, 2004 now U.S. Pat. No. 7,930,540, entitled “CRYPTOGRAPHICPOLICY ENFORCEMENT,” Inventor(s) Ratinder Paul Singh Ahuja, et al.,which claims the priority benefit of U.S. Provisional Application60/538,582, entitled “ENCRYPTION DETECTION IN A DATA CAPTURE ANDANALYSIS SYSTEM”, filed Jan. 22, 2004. The disclosure of the priorapplication is considered part of (and is incorporated by reference in)the disclosure of this application.

FIELD OF THE INVENTION

The present invention relates to computer networks, and in particular,to enforcing a cryptographic policy over a computer network.

BACKGROUND

Computer networks and systems have become indispensable tools for modernbusiness. Modern enterprises use such networks for communications andfor storage. The information and data stored on the network of abusiness enterprise is often a highly valuable asset. Modern enterprisesuse numerous tools to keep outsiders, intruders, and unauthorizedpersonnel from accessing valuable information stored on the network.These tools include firewalls, intrusion detection systems, and packetsniffer devices. However, once an intruder has gained access tosensitive content, there is no network device that can prevent theelectronic transmission of the content from the network to outside thenetwork. Similarly, there is no network device that can analyse the dataleaving the network to monitor for policy violations, and make itpossible to track down information leeks. What is needed is acomprehensive system to capture, store, and analyse all datacommunicated using the enterprise's network.

SUMMARY OF THE INVENTION

Objects can be extracted from data flows captured by a capture device.In one embodiment, the invention includes assigning to each capturedobject a cryptographic status based on whether the captured object isencrypted. In one embodiment, the invention further includes determiningwhether the object violated a cryptographic policy using the assignedcryptographic status of the object.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements and in which:

FIG. 1 is a block diagram illustrating a computer network connected tothe Internet;

FIG. 2 is a block diagram illustrating one configuration of a capturesystem according to one embodiment of the present invention;

FIG. 3 is a block diagram illustrating the capture system according toone embodiment of the present invention;

FIG. 4 is a block diagram illustrating an object assembly moduleaccording to one embodiment of the present invention;

FIG. 5 is a block diagram illustrating an object store module accordingto one embodiment of the present invention;

FIG. 6 is a block diagram illustrating an example hardware architecturefor a capture system according to one embodiment of the presentinvention; and

FIG. 7 is a flow diagram illustrating cryptographic policy enforcementaccording to one embodiment of the present invention.

DETAILED DESCRIPTION

Although the present system will be discussed with reference to variousillustrated examples, these examples should not be read to limit thebroader spirit and scope of the present invention. Some portions of thedetailed description that follows are presented in terms of algorithmsand symbolic representations of operations on data within a computermemory. These algorithmic descriptions and representations are the meansused by those skilled in the computer science arts to most effectivelyconvey the substance of their work to others skilled in the art. Analgorithm is here, and generally, conceived to be a self-consistentsequence of steps leading to a desired result. The steps are thoserequiring physical manipulations of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared and otherwise manipulated.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers or the like. It should be borne in mind,however, that all of these and similar terms are to be associated withthe appropriate physical quantities and are merely convenient labelsapplied to these quantities. Unless specifically stated otherwise, itwill be appreciated that throughout the description of the presentinvention, use of terms such as “processing”, “computing”,“calculating”, “determining”, “displaying” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system's registersand memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

As indicated above, one embodiment of the present invention isinstantiated in computer software, that is, computer readableinstructions, which, when executed by one or more computerprocessors/systems, instruct the processors/systems to perform thedesignated actions. Such computer software may be resident in one ormore computer readable media, such as hard drives, CD-ROMs, DVD-ROMs,read-only memory, read-write memory and so on. Such software may bedistributed on one or more of these media, or may be made available fordownload across one or more computer networks (e.g., the Internet).Regardless of the format, the computer programming, rendering andprocessing techniques discussed herein are simply examples of the typesof programming, rendering and processing techniques that may be used toimplement aspects of the present invention. These examples should in noway limit the present invention, which is best understood with referenceto the claims that follow this description.

Networks

FIG. 1 illustrates a simple prior art configuration of a local areanetwork (LAN) 10 connected to the Internet 12. Connected to the LAN 102are various components, such as servers 14, clients 16, and switch 18.There are numerous other known networking components and computingdevices that can be connected to the LAN 10. The LAN 10 can beimplemented using various wireline or wireless technologies, such asEthernet and 802.11b. The LAN 10 may be much more complex than thesimplified diagram in FIG. 1, and may be connected to other LANs aswell.

In FIG. 1, the LAN 10 is connected to the Internet 12 via a router 20.This router 20 can be used to implement a firewall, which are widelyused to give users of the LAN 10 secure access to the Internet 12 aswell as to separate a company's public Web server (can be one of theservers 14) from its internal network, i.e., LAN 10. In one embodiment,any data leaving the LAN 10 towards the Internet 12 must pass throughthe router 12. However, there the router 20 merely forwards packets tothe Internet 12. The router 20 cannot capture, analyze, and searchablystore the content contained in the forwarded packets.

One embodiment of the present invention is now illustrated withreference to FIG. 2. FIG. 2 shows the same simplified configuration ofconnecting the LAN 10 to the Internet 12 via the router 20. However, inFIG. 2, the router 20 is also connected to a capture system 22. In oneembodiment, the router 12 splits the outgoing data stream, and forwardsone copy to the Internet 12 and the other copy to the capture system 22.

There are various other possible configurations. For example, the router12 can also forward a copy of all incoming data to the capture system 22as well. Furthermore, the capture system 22 can be configuredsequentially in front of, or behind the router 20, however this makesthe capture system 22 a critical component in connecting to the Internet12. In systems where a router 12 is not used at all, the capture systemcan be interposed directly between the LAN 10 and the Internet 12. Inone embodiment, the capture system 22 has a user interface accessiblefrom a LAN-attached device, such as a client 16.

In one embodiment, the capture system 22 intercepts all data leaving thenetwork. In other embodiments, the capture system can also intercept alldata being communicated inside the network 10. In one embodiment, thecapture system 22 reconstructs the documents leaving the network 10, andstores them in a searchable fashion. The capture system 22 can then beused to search and sort through all documents that have left the network10. There are many reasons such documents may be of interest, includingnetwork security reasons, intellectual property concerns, corporategovernance regulations, and other corporate policy concerns.

Capture System

One embodiment of the present invention is now described with referenceto FIG. 3. FIG. 3 shows one embodiment of the capture system 22 in moredetail. The capture system 22 includes a network interface module 24 toreceive the data from the network 10 or the router 20. In oneembodiment, the network interface module 24 is implemented using one ormore network interface cards (NIC), e.g., Ethernet cards. In oneembodiment, the router 20 delivers all data leaving the network to thenetwork interface module 24.

The captured raw data is then passed to a packet capture module 26. Inone embodiment, the packet capture module 26 extracts data packets fromthe data stream received from the network interface module 24. In oneembodiment, the packet capture module 26 reconstructs Ethernet packetsfrom multiple sources to multiple destinations for the raw data stream.

In one embodiment, the packets are then provided the object assemblymodule 28. The object assembly module 28 reconstructs the objects beingtransmitted by the packets. For example, when a document is transmitted,e.g. as an email attachment, it is broken down into packets according tovarious data transfer protocols such as Transmission ControlProtocol/Internet Protocol (TCP/IP) and Ethernet. The object assemblymodule 28 can reconstruct the document from the captured packets.

One embodiment of the object assembly module 28 is now described in moredetail with reference to FIG. 4. When packets first enter the objectassembly module, they are first provided to a reassembler 36. In oneembodiment, the reassembler 36 groups—assembles—the packets into uniqueflows. For example, a flow can be defined as packets with identicalSource IP and Destination IP addresses as well as identical TCP Sourceand Destination Ports. That is, the reassembler 36 can organize a packetstream by sender and recipient.

In one embodiment, the reassembler 36 begins a new flow upon theobservation of a starting packet defined by the data transfer protocol.For a TCP/IP embodiment, the starting packet is generally referred to asthe “SYN” packet. The flow can terminate upon observation of a finishingpacket, e.g., a “Reset” or “FIN” packet in TCP/IP. If now finishingpacket is observed by the reassembler 36 within some time constraint, itcan terminate the flow via a timeout mechanism. In an embodiment usingthe TPC protocol, a TCP flow contains an ordered sequence of packetsthat can be assembled into a contiguous data stream by the reassembler36. Thus, in one embodiment, a flow is an ordered data stream of asingle communication between a source and a destination.

The flown assembled by the reassembler 36 can then be provided to aprotocol demultiplexer (demux) 38. In one embodiment, the protocol demux38 sorts assembled flows using the TCP Ports. This can includeperforming a speculative classification of the flow contents based onthe association of well-known port numbers with specified protocols. Forexample, Web Hyper Text Transfer Protocol (HTTP) packets—i.e., Webtraffic—are typically associated with port 80, File Transfer Protocol(FTP) packets with port 20, Kerberos authentication packets with port88, and so on. Thus in one embodiment, the protocol demux 38 separatesall the different protocols in one flow.

In one embodiment, a protocol classifier 40 also sorts the flows inaddition to the protocol demux 38. In one embodiment, the protocolclassifier 40—operating either in parallel or in sequence with theprotocol demux 38—applies signature filters to the flows to attempt toidentify the protocol based solely on the transported data. Furthermore,the protocol demux 38 can make a classification decision based on portnumber, which is subsequently overridden by protocol classifier 40. Forexample, if an individual or program attempted to masquerade an illicitcommunication (such as file sharing) using an apparently benign portsuch as port 80 (commonly used for HTTP Web browsing), the protocolclassifier 40 would use protocol signatures, i.e., the characteristicdata sequences of defined protocols, to verify the speculativeclassification performed by protocol demux 38.

In one embodiment, the object assembly module 28 outputs each floworganized by protocol, which represent the underlying objects. Referringagain to FIG. 3, these objects can then be handed over to the objectclassification module 30 (sometimes also referred to as the “contentclassifier”) for classification based on content. A classified flow maystill contain multiple content objects depending on the protocol used.For example, protocols such as HTTP (Internet Web Surfing) may containover 100 objects of any number of content types in a single flow. Todeconstruct the flow, each object contained in the flow is individuallyextracted, and decoded, if necessary, by the object classificationmodule 30.

The object classification module 30 uses the inherent properties andsignatures of various documents to determine the content type of eachobject. For example, a Word document has a signature that is distinctfrom a PowerPoint document, or an Email document. The objectclassification module 30 can extract out each individual object and sortthem out by such content types. Such classification renders the presentinvention immune from cases where a malicious user has altered a fileextension or other property in an attempt to avoid detection of illicitactivity.

In one embodiment, the object classification module 30 determineswhether each object should be stored or discarded. In one embodiment,this determination is based on a various capture rules. For example, acapture rule can indicate that Web Traffic should be discarded. Anothercapture rule can indicate that all PowerPoint documents should bestored, except for ones originating from the CEO's IP address. Suchcapture rules can be implemented as regular expressions, or by othersimilar means. Several embodiments of the object classification module30 are described in more detail further below.

In one embodiment, the capture rules are authored by users of thecapture system 22. The capture system 22 is made accessible to anynetwork-connected machine through the network interface module 24 anduser interface 34. In one embodiment, the user interface 34 is agraphical user interface providing the user with friendly access to thevarious features of the capture system 22. For example, the userinterface 34 can provide a capture rule authoring tool that allows usersto write and implement any capture rule desired, which are then appliedby the object classification module 30 when determining whether eachobject should be stored. The user interface 34 can also providepre-configured capture rules that the user can select from along with anexplanation of the operation of such standard included capture rules. Inone embodiment, the default capture rule implemented by the objectclassification module 30 captures all objects leaving the network 10.

If the capture of an object is mandated by the capture rules, the objectclassification module 30 can also determine where in the object storemodule 32 the captured object should be stored. With reference to FIG.5, in one embodiment, the objects are stored in a content store 44memory block. Within the content store 44 are files 46 divided up bycontent type. Thus, for example, if the object classification moduledetermines that an object is a Word document that should be stored, itcan store it in the file 46 reserved for Word documents. In oneembodiment, the object store module 32 is integrally included in thecapture system 22. In other embodiments, the object store module can beexternal—entirely or in part—using, for example, some network storagetechnique such as network attached storage (NAS) and storage areanetwork (SAN).

Tag Data Structure

In one embodiment, the content store is a canonical storage location,simply a place to deposit the captured objects. The indexing of theobjects stored in the content store 44 is accomplished using a tagdatabase 42. In one embodiment, the tag database 42 is a database datastructure in which each record is a “tag” that indexes an object in thecontent store 44 and contains relevant information about the storedobject. An example of a tag record in the tag database 42 that indexesan object stored in the content store 44 is set forth in Table 1:

TABLE 1 Field Name Definition MAC Address Ethernet controller MACaddress unique to each capture system Source IP Source Ethernet IPAddress of object Destination IP Destination Ethernet IP Address ofobject Source Port Source TCP/IP Port number of object Destination PortDestination TCP/IP Port number of the object Protocol IP Protocol thatcarried the object Instance Canonical count identifying object within aprotocol capable of carrying multiple data within a single TCP/IPconnection Content Content type of the object Encoding Encoding used bythe protocol carrying object Size Size of object Timestamp Time that theobject was captured Owner User requesting the capture of object (ruleauthor) Configuration Capture rule directing the capture of objectSignature Hash signature of object Tag Signature Hash signature of allpreceding tag fields

There are various other possible tag fields, and some embodiments canomit numerous tag fields listed in Table 1. In other embodiments, thetag database 42 need not be implemented as a database, and a tag neednot be a record. Any data structure capable of indexing an object bystoring relational data over the object can be used as a tag datastructure. Furthermore, the word “tag” is merely descriptive, othernames such as “index” or “relational data store,” would be equallydescriptive, as would any other designation performing similarfunctionality.

The mapping of tags to objects can, in one embodiment, be obtained byusing unique combinations of tag fields to construct an object's name.For example, one such possible combination is an ordered list of theSource IP, Destination IP, Source Port, Destination Port, Instance andTimestamp. Many other such combinations including both shorter andlonger names are possible. In another embodiment, the tag can contain apointer to the storage location where the indexed object is stored.

The tag fields shown in Table 1 can be expressed more generally, toemphasize the underlying information indicated by the tag fields invarious embodiments. Some of these possible generic tag fields are setforth in Table 2:

TABLE 2 Field Name Definition Device Identity Identifier of capturedevice Source Address Origination Address of object Destination AddressDestination Address of object Source Port Origination Port of objectDestination Port Destination Port of the object Protocol Protocol thatcarried the object Instance Canonical count identifying object within aprotocol capable of carrying multiple data within a single connectionContent Content type of the object Encoding Encoding used by theprotocol carrying object Size Size of object Timestamp Time that theobject was captured Owner User requesting the capture of object (ruleauthor) Configuration Capture rule directing the capture of objectSignature Signature of object Tag Signature Signature of all precedingtag fields

For many of the above tag fields in Tables 1 and 2, the definitionadequately describes the relational data contained by each field. Forthe content field, the types of content that the object can be labeledas are numerous. Some example choices for content types (as determined,in one embodiment, by the object classification module 30) are JPEG,GIF, BMP, TIFF, PNG (for objects containing images in these variousformats); Skintone (for objects containing images exposing human skin);PDF, MSWord, Excel, PowerPoint, MSOffice (for objects in these popularapplication formats); HTML, WebMail, SMTP, FTP (for objects captured inthese transmission formats); Telnet, Rlogin, Chat (for communicationconducted using these methods); GZIP, ZIP, TAR (for archives orcollections of other objects); Basic_Source, C++_Source, C_Source,Java_Source, FORTRAN_Source, Verilog_Source, VHDL_Source,Assembly_Source, Pascal_Source, Cobol_Source, Ada_Source, Lisp_Source,Perl_Source, XQuery_Source, Hypertext Markup Language, Cascaded StyleSheets, JavaScript, DXF, Spice, Gerber, Mathematica, Matlab, AllegroPCB,ViewLogic, TangoPCAD, BSDL, C_Shell, K_Shell, Bash_Shell, Bourne_Shell,FTP, Telnet, MSExchange, POP3, RFC822, CVS, CMS, SQL, RTSP, MIME, PDF,PS (for source, markup, query, descriptive, and design code authored inthese high-level programming languages); C Shell, K Shell, Bash Shell(for shell program scripts); Plaintext (for otherwise unclassifiedtextual objects); Crypto (for objects that have been encrypted or thatcontain cryptographic elements); Englishtext, Frenchtext, Germantext,Spanishtext, Japanesetext, Chinesetext, Koreantext, Russiantext (anyhuman language text); Binary Unknown, ASCII Unknown, and Unknown (ascatchall categories).

The signature contained in the Signature and field can be any digest orhash over the object, or some portion thereof, and the Tag Signature cansimilarly be any such digest or hash over the other tag fields or aportion thereof. In one embodiment, a well-known hash, such as MD5 orSHA 1 can be used. In one embodiment, the Signature is a digitalcryptographic signature. In one embodiment, a digital cryptographicsignature is a hash signature that is signed with the private key of thecapture system 22. Only the capture system 22 knows its own private key,thus, the integrity of the stored object can be verified by comparing ahash of the stored object to the signature decrypted with the public keyof the capture system 22, the private and public keys being a public keycryptosystem key pair. Thus, if a stored object is modified from when itwas originally captured, the modification will cause the comparison tofail.

Similarly, the signature over the tag stored in the Tag Signature fieldcan also be a digital cryptographic signature. In such an embodiment,the integrity of the tag can also be verified. In one embodiment,verification of the object using the signature, and the tag using thetag signature is performed whenever an object is presented, e.g.,displayed to a user. In one embodiment, if the object or the tag isfound to have been compromised, an alarm is generated to alert the userthat the object displayed may not be identical to the object originallycaptured.

Encryption Detection and Cryptographic Policy Enforcement

In one embodiment, the capture system 22 described with reference toFIG. 3 also includes a cryptographic analyzer 35 (also referred to asthe “crypto analyzer” for short). Referring again to FIG. 3, the cryptoanalyzer 35 is configured, in one embodiment, to scan the reassembledand captured objects to determine whether the objects are encrypted. Inone embodiment, the crypto analyzer 35 does not determine whether theflows were sent using real-time encryption protocol (such as SecureSockets Layer), but only determines whether the objects themselves wereencrypted using some form of block cipher prior to transmission. Inanother embodiment, real-time protocol encryption is also detected bythe crypto analyzer 35.

In one embodiment, the crypto analyzer 35 does not need to rely on anyknown content in the objects to detect whether they have been encrypted.There are various encryption detection methods that can be used toimplement the crypto analyzer 35. For example, one method of detectingencrypted data to is to use the highly random nature of ciphertext tocreate a statistical method for distinguishing ciphertext from otherbinary data. For encrypted bytes, all the byte values from 0 to 255 willoccur roughly the same number of times. For ASCII English documents thebyte values above 128 rarely occur (only for formatting information),and most the byte values correspond to lower case letters. Binaryformats such as compiled code for the CPU, or byte codes the Java, ordatabase files for Oracle, also show a strongly non-uniform distributionof bytes.

In one embodiment, the crypto analyzer 35 uses a statistical measurebased on a frequency count of the bytes that appear in a block of aparticular size. For example, a block of 2048 bytes could be processedto produce a table that counted the number of times each byte valueoccurred (e.g., how many out of the 2048 bytes were zero, one, two, andso on, up to 255). A visual histogram of this table will show a fairlyflat distribution if the data is encrypted, and a dramatically spikydistribution if the data is not encrypted.

For encrypted data, each byte value can be expected to appear 8 times onaverage (because 8 is 2048/256), with an expected statistical varianceof about 3. This means that roughly 67% percent (the percentage ofrandom values that fall within one standard deviation of the mean) ofthe counts will be between 5 and 11. For ASCII text, the values between128 and 255 never appear, so some of the values between 0 and 127 isexpected to occur well over 16 times.

In one embodiment, the crypto analyzer 35 uses a statistical measureknown as the Index of Coincidence (IC) to detect encryption of objects.The IC is closely related to the statistical measure known as variance.The IC measures how much a distribution of numbers differs from beingperfectly uniform (a flat histogram).

For example, define F[j] to be the frequency that the observed byteswere equal to j, that is, F[j] is a count of the number of times thatthe byte value, j, appeared in the block being tested. Define S to bethe sum of the square of the F values: S=Sum(j=0 to 255, F[j]*F[j]).Define T to be S divided by the square of the block size: T=S/(N*N). Forexample N is 2048 if the block being tested contained 2048 bytes. DefineC to be a cut off value (discussed below). We will declare that the datais Encrypted if T<C. Larger values of T indicate non-random data, whichdeclare as Unencrypted. The cut off value C is defined relative to theexpected value of T when the bytes are truly random. If all F[j] valuesare equal, then T=256*(( 1/256)*( 1/256))= 1/256. The expected standarddeviation for T (assuming random bytes) is also 1/256. To allow for theT statistical measure to accept random bytes that are three standarddeviations away the expected value, the C should be 4/256 (mean plusthree time standard deviations).

For many unencrypted binary data types, such as compressed files orsound files, a statistic encompassing the whole file would likely showall the possible byte values appearing roughly an equal number of times.However, with these types of binary files, there is a very strongcorrelation between near-by bytes. For example, a sound file in 8-bitWAV format exhibits a strong tendency for adjacent bytes to havenumerical values that are close together because the amplitude of thesound wave being sampled varies slowly over the sampling time. Thisphenomenon also shows up in compressed files, executables, and byte codefiles.

In one embodiment, the crypto analyzer 35 improves the accuracy of thetest by pre-processing the bytes before performing the appropriatestatistical analysis. Rather than performing the test on the raw bytes,perform the test on the difference between adjacent bytes. For trulyrandom bytes, this pre-processing will not make any difference (thedifference (or sum) of two high entropy values is also a high entropyvalue). In one embodiment, such pre-processing will amplify thestructured relationship between adjacent bytes for non-encrypted data.

For example, for the case of 16-bit sound files, the correlation betweenvalues shows up between every other byte. The test can examine both thedifference between the even numbered bytes and the odd numbered bytes.

As a further example, some image files have 24-bit samples sizes (onebyte each for Red, Yellow, and Green), and some data files have 32-bitsample sizes. In this case, a tool can examine differences between bytesthat are one, two, three or four locations apart.

The S and T statistics discussed above generally work well for largesample sizes. However, if there were a small number of samples (the F[j]values were generally less than 5), the crypto analyzer 35 can switch tousing an unbiased statistic, here referred to as S1 and T1. In oneembodiment, S1 is defined as Sum(j=0 to 255, F[j]*(F[j]−1)), and T1 isdefined as S1/(N*(N−1)). In one embodiment, the cut off value C is thesame.

There are various other statistics and methods that can be used todetermine whether a captured and reconstructed object is encrypted ornot. In one embodiment, the crypto analyzer 35 also identifies the typeof encryption used. In yet another embodiment, the crypto analyzer 35also attempt to decrypt the object if possible.

In one embodiment, the capture system 22 uses the crypto analyzer 35 toimplement a cryptographic policy over the network 10 monitored by thecapture device. One embodiment of implementing such a policy is nowdescribed with reference to FIG. 7. In block 102, the packets traversingthe network are captured and organized into flows as described above. Inblock 104, an object is assembled from the captured packets as describedabove.

In block 106, a cryptographic status is assigned to the object. In oneembodiment, the cryptographic status can be either “encrypted” or“not-encrypted.” In one embodiment, the cryptographic status is alsostored in the tag associated with the object in the tag database. Thetag field could be called “Encrypted” and the value could be either YESor NO. In other embodiments, the specific kind of encryption can also beindicated in this field. In one embodiment, the cryptographic status isassigned by performing a statistical encryption detection on the objectas described above.

Once the object is classified as encrypted or not, in block 108, it isdetermined whether the transmission of the object as intercepted by thecapture system 22 violates the cryptographic policy in effect over thenetwork 10. In one embodiment, the cryptographic policy consists of aset of rules, also known as cryptographic rules. The cryptographic rulesdetail what transmissions must be encrypted, what transmissions may notbe encrypted, and what transmissions allow but do not requireencryption.

For example, one rule can forbid the encryption of objects between twodepartments. Another rule may require an encrypted channel between twoother departments. The departments, and consequently the channels oftransmission can be defined using lists or ranges of IP addresses.Specific IP addresses can be singled out as not being permitted to sendencrypted data in any channel.

In one embodiment, the rules that make up the cryptographic policy arecreated by a user or group of users via the user interface 34. Referringagain to FIG. 3, the rules of the cryptographic policy are maintained,in one embodiment, by the crypto analyser 35. The rules can be createdor edited using the user interface 34.

In one embodiment, the user interface 34 includes a graphicalcryptographic policy editor and rule author that simplifies thecryptographic rule writing. For example, the editor may allow the userto visually define an interdepartmental channel, and specify the policy(e.g., “must be encrypted channel”) for the channel. The editor can haveother features allowing a user to tailor the rules of the policy to theparticular specifications of the policy.

In one embodiment, the crypto analyser 35 performs cryptographic statusassignment and policy enforcement on objects being assembled in realtime. In another embodiment, however, the crypto analyser 35 can alsofunction in an “off-line” mode, i.e., on objects already stored in theobject store module 32. In yet another embodiment, part of the objectsare analysed in real-time, and those that are not (e.g., because ofspeed constraints) are analysed later.

In one embodiment, the crypto analyzer 35 responds to detecting a policyviolation (that is a violation of one of the rules) by generating analert. The alert can be directed to a designated system administrator orgroup, or the user who authored the specific rule being violated by thecaptured object. The alert can be delivered via the user interface 34,and may include any information about the object contained in the tagassociated with the object and the specific cryptographic rule violatedby the object.

In one embodiment, the capture system 22 halts delivery of any objectsviolating the cryptographic policy. When this happens, the capturesystem 22 can alert the sender of the object (as defined by the sourceIP) that he or she is violating encryption policy, and that the capturedtransaction should be repeated in compliance with the policy.

General Matters

In several embodiments, the capture system 22 has been described aboveas a stand-alone device. However, the capture system of the presentinvention can be implemented on any appliance capable of capturing andanalyzing data from a network. For example, the capture system 22described above could be implemented on one or more of the servers 14 orclients 16 shown in FIG. 1. The capture system 22 can interface with thenetwork 10 in any number of ways, including wirelessly.

In one embodiment, the capture system 22 is an appliance constructedusing commonly available computing equipment and storage systems capableof supporting the software requirements. In one embodiment, illustratedby FIG. 6, the hardware consists of a capture entity 46, a processingcomplex 48 made up of one or more processors, a memory complex 50 madeup of one or more memory elements such as RAM and ROM, and storagecomplex 52, such as a set of one or more hard drives or other digital oranalog storage means. In another embodiment, the storage complex 52 isexternal to the capture system 22, as explained above. In oneembodiment, the memory complex stored software consisting of anoperating system for the capture system device 22, a capture program,and classification program, a database, a filestore, an analysis engineand a graphical user interface.

Thus, a capture system and an object presentation procedure have beendescribed. In the forgoing description, various specific values weregiven names, such as “objects” and “tags,” and various specific modules,such as the “crypto analyzer” and “user interface” have been described.However, these names are merely to describe and illustrate variousaspects of the present invention, and in no way limit the scope of thepresent invention. Furthermore, various modules, such as the cryptoanalyzer 35 in FIG. 3, can be implemented as software or hardwaremodules, or without dividing their functionalities into modules at all.The present invention is not limited to any modular architecture eitherin software or in hardware, whether described above or not.

1. A method, comprising: capturing packets in a network environment;assembling an object from the captured packets; determining whether theobject is encrypted; assigning a cryptographic status to the object; anddetermining whether the object violated a cryptographic policy, whereinthe cryptographic policy comprises a set of cryptographic rulesdifferentiating between transmissions that are required to be encrypted,transmissions that are required to be unencrypted, and transmissionsthat are allowed, but not required, to be encrypted.
 2. The method ofclaim 1, wherein the cryptographic status is provided for the objectbased, at least, on a statistical analysis of bytes in the object. 3.The method of claim 2, wherein determining whether the object violatedthe cryptographic policy comprises determining that the object wasunencrypted when the object was required to be encrypted.
 4. The methodof claim 2, further comprising: generating an alert if the cryptographicpolicy is determined to be violated.
 5. The method of claim 1, whereinthe statistical analysis comprises calculating an index of coincidencefor the bytes in the captured object.
 6. The method of claim 5, whereinthe index of coincidence measures a distribution of numbers that differfrom being uniform.
 7. The method of claim 1, further comprising:evaluating whether the object was encrypted using a form of block cipherprior to transmission.
 8. The method of claim 1, wherein the statisticalanalysis includes distinguishing ciphertext from binary data.
 9. Themethod of claim 1, wherein the statistical analysis includes identifyinga frequency count of bytes that appear in a block of bytes of aparticular size.
 10. The method of claim 1, wherein the bytes arepreprocessed before performing the statistical analysis such that thestatistical analysis is based at least on differences between adjacentbytes.
 11. The method of claim 1, further comprising: identifying a typeof encryption used for the object; and decrypting the object.
 12. Themethod of claim 1, wherein the cryptographic status is stored in a tagassociated with the object, and wherein the tag is provided in a tagdatabase.
 13. A capture system, comprising: a packet capture moduleconfigured to capture packets in a network environment; an objectassembly module configured to assemble an object from the capturedpackets; and a cryptographic analyzer configured to determine acryptographic status of the object and determine whether the objectviolated a cryptographic policy, wherein the cryptographic policycomprises a set of cryptographic rules differentiating betweentransmissions that are required to be encrypted, transmissions that arerequired to be unencrypted, and transmissions that are allowed, but notrequired, to be encrypted.
 14. The capture system of claim 13, whereinthe statistical analysis comprises calculating an index of coincidencefor the bytes in the captured object, and wherein the index ofcoincidence measures a distribution of numbers that differ from beinguniform.
 15. The capture system of claim 13, wherein the statisticalanalysis includes distinguishing ciphertext from binary data, andwherein the statistical analysis includes identifying a frequency countof bytes that appear in a block of bytes of a particular size.
 16. Thecapture system of claim 13, wherein the bytes are preprocessed beforeperforming the statistical analysis such that the statistical analysisis based at least on differences between adjacent bytes.
 17. Anon-transitory medium having stored thereon data representinginstructions configured for execution by a processor of a capturesystem, the instructions causing the capture system to performoperations comprising: assembling an object from packets captured in anetwork environment; determining whether the object is encrypted;identifying a cryptographic status of the object; and determiningwhether the object violated a cryptographic policy, wherein thecryptographic policy comprises a set of cryptographic rulesdifferentiating between transmissions that are required to be encrypted,transmissions that are required to be unencrypted, and transmissionsthat are allowed, but not required, to be encrypted.
 18. The medium ofclaim 17, wherein determining whether the object violated thecryptographic policy comprises determining that the object wasunencrypted when the object was required to be encrypted.
 19. The mediumof claim 17, wherein the statistical analysis comprises calculating anindex of coincidence for the bytes in the captured object, and whereinthe index of coincidence measures a distribution of numbers that differfrom being uniform.
 20. The medium of claim 17, wherein the statisticalanalysis includes distinguishing ciphertext from binary data, andwherein the statistical analysis includes identifying a frequency countof bytes that appear in a block of bytes of a particular size.